One AEO Playbook Won't Work: ChatGPT, Claude, Perplexity & Google Are Different Games

TL;DR: Teams keep treating answer engine optimization (AEO) like SEO — one playbook, run everywhere. In a recent Distribution Podcast conversation, Ravish Agrawal of Gamma put a number on why that fails: only ~30–40% of the AEO playbook overlaps across the four major AI engines, meaning roughly 60% of the work is platform-specific (both his practitioner estimates — heuristics, not benchmarks). ChatGPT rewards Reddit and community presence; Perplexity leans on third-party authority; Claude is won through its ecosystem, not its citations; Google AI Overviews behave like a faster version of classic search. Below: what Ravish attributes to each surface, and how to run separate prompt sets and reporting per platform. Every number is attributed to Ravish/Gamma or the host — none is Asva data, and several carry verify-before-publishing flags.

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Why there's no single playbook

The intuitive move is to build one AEO checklist and ship it against ChatGPT, Perplexity, Gemini/Google, and Claude at once. Ravish Agrawal of Gamma argues that assumption quietly wastes most of your effort.

His framing: only ~30–40% of the playbook overlaps across the four engines, so roughly 60% differs by platform (Ravish's practitioner estimates from the podcast — the 30–40% is a stated heuristic; the ~60% is the inferred complement, so read it as "roughly"). The overlapping third is the fundamentals you'd expect — clear, well-structured content, strong entity signals, being genuinely useful on the question being asked. The other ~60% is where each engine sources, weighs, and refreshes information differently enough that a tactic which wins on one surface does little on another.

On the AI-referral side, Ravish said Gamma's ChatGPT-referred traffic grew roughly 8× over a period (attribute to Ravish/Gamma; the exact date range and baseline still need confirmation) — while click-through share stayed under 1% (his figure; the denominator was left unclear, so don't recast it as a share of users or revenue). Growth and share moved on different clocks — which is the whole argument for measuring, and optimizing, each engine on its own terms.

Our answer engine optimization guide and generative engine optimization guide cover the shared fundamentals; the sections below cover the ~60% that splits by platform.

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ChatGPT: community and Reddit do the heavy lifting

Ravish's strongest platform-specific claim was about ChatGPT: Reddit is the most influential source for ChatGPT specifically — not for every engine — and he tied that to the Reddit–OpenAI data licensing deal. On ChatGPT, community consensus and Reddit threads carry weight they don't automatically carry elsewhere.

Two more attributed points sharpen the picture:

• Prosumer demand concentrates on ChatGPT. Ravish estimated that north of 90% of Gamma's AI/LLM acquisition came from ChatGPT in the prosumer segment (his estimate; confirm the exact metric definition and date range before repeating). For consumer and prosumer brands, ChatGPT is often where the volume is.
• Reddit citations are slow to form. Ravish observed new Reddit content taking 30–45 days to surface as a ChatGPT citation (an observed range, not a guarantee). ChatGPT rewards durable community presence, not last-minute posting.

Practical read: for ChatGPT, invest in authentic Reddit and community footprint and expect a multi-week lag before it shows up.

Google AI Overviews: classic search, accelerated

Google AI Overviews behaved, in the podcast's anecdotes, like the fastest-moving surface. The host recounted a niche Reddit query getting cited in a Google AI Overview in one day (a single host observation, anecdotal). That speed lines up with Google's existing crawl-and-index machinery — the overview layer sits on top of infrastructure that already refreshes quickly.

The takeaway isn't "Google is solved." It's that much of your traditional technical SEO and structured-content work transfers here more directly than to any other AI surface — which is exactly why it falls partly inside the ~30–40% overlap. If you want to pressure-test readiness for Google's AI surfaces specifically, our Gemini optimizer is built for that layer.

Perplexity: third-party authority wins

For Perplexity, Ravish's attributed point was about whose content gets trusted: Perplexity gives more authority to third-party sources than to your own domain. Where ChatGPT leans on community and Google leans on its index, Perplexity behaves more like a research assistant that prefers independent, citable coverage of you over your own marketing pages.

Perplexity was also the fastest to pick up a specific new phrase in Gamma's experience — Ravish recalled it surfacing a new phrase as fast as the next day (anecdotal, a single observation). So Perplexity is quick, but it rewards a different asset class: earned third-party mentions, reviews, and coverage rather than owned content alone. That makes digital PR and getting cited by others a Perplexity-first tactic.

Claude: win the ecosystem, not the citation

Claude is the outlier, and Ravish's framing here is the most counterintuitive. He recalled studies suggesting Claude cites roughly 5× less than ChatGPT — ⚠️ VERIFY BEFORE PUBLISHING: this is a recalled third-party statistic with no named source; do not present it as established fact. If it holds, it means chasing Claude citations the way you chase ChatGPT citations is largely wasted effort.

Instead, Ravish offered an analogy worth labeling as exactly that — an analogy, not a measured claim: Claude behaves like an app ecosystem or app store, while ChatGPT behaves more like Google Search. The implication is that Claude visibility is won less through cited web content and more through its ecosystem and tool integrations — showing up where Claude connects to tools and apps, rather than ranking to be quoted.

This is also why segment matters. Ravish put the prosumer engine priority as ChatGPT → Google AI Overviews → Perplexity → Claude. For enterprise, he suggested the order flips toward Claude → Copilot/Bing → the rest — but he flagged this as speculative, so treat it as a hypothesis to test against your own audience, not a rule.

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The four engines at a glance

Platform	What wins (attributed to Ravish/Gamma)	Primary asset	Speed to cite	Notes
ChatGPT	Reddit + community presence (Reddit–OpenAI data deal)	Community footprint, Reddit threads	~30–45 days for Reddit content	>90% of Gamma's prosumer AI acquisition (his estimate)
Google AI Overviews	Classic SEO signals, structured content	Indexed, well-structured owned content	As fast as one day (host anecdote)	Behaves like accelerated traditional search
Perplexity	Third-party authority over owned content	Earned mentions, reviews, digital PR	As fast as next day (anecdotal)	Prefers independent sources about you
Claude	Ecosystem / tool integrations, not citations	Ecosystem presence ("app store" analogy)	Cites ~5× less than ChatGPT (⚠️ verify)	Rises in enterprise priority (speculative)

All figures attributed to Ravish Agrawal of Gamma or the podcast host. None is Asva data. Rows marked ⚠️ require verification before external citation.

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How to run platform-specific AEO

If ~60% of the work differs by engine, your operating model has to differ too. Three shifts:

1. Separate prompt sets per platform. Don't test one prompt list against all engines and average the results — that hides the ~60% that diverges. Build distinct prompt sets that reflect how each surface is actually queried, and run them against each engine independently. Track appearances per platform, not a single blended "AI visibility" number.

2. Report per platform, not in aggregate. A blended score masks exactly the divergence Ravish is describing. You want to see, per engine, whether you're being mentioned, cited, or ignored — and how that changes week over week. Asva's brand visibility tracker is designed for this per-engine view across ChatGPT, Perplexity, Gemini, Google AI Mode, Copilot, and more, so you can act on each surface separately.

3. Match the asset to the engine. Community and Reddit for ChatGPT. Structured, indexable owned content for Google AI Overviews. Third-party coverage and earned mentions for Perplexity. Ecosystem and integration presence for Claude. The same content investment produces very different returns depending on where you point it.

Want a per-engine snapshot of where you stand today? Run a free AI Visibility Report to see how each platform currently treats your brand.

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FAQ

Q: Is there really no single AEO playbook that works everywhere?

A: There's a shared core, but it's minority. Ravish Agrawal of Gamma estimates only ~30–40% of the playbook overlaps across ChatGPT, Claude, Perplexity, and Google — meaning roughly 60% is platform-specific. Both figures are his practitioner heuristics, not industry benchmarks. Treat the overlapping ~30–40% as fundamentals and budget separately for the rest.

Q: Why is Reddit tied so closely to ChatGPT specifically?

A: Ravish attributes it to the Reddit–OpenAI data licensing deal, which gives Reddit content outsized influence on ChatGPT relative to other engines. He was explicit that this is a ChatGPT-specific effect, not a universal ranking factor across all AI surfaces.

Q: Does Claude really cite about 5× less than ChatGPT?

A: Ravish recalled studies suggesting this, but the source was unnamed — so we flag it as unverified and you should confirm it before relying on it. His more actionable point is strategic: Claude behaves more like an app ecosystem than a search engine, so it's won through tool and ecosystem integrations rather than through cited content.

Q: Which platform should a prosumer brand prioritize first?

A: Ravish's stated prosumer priority order is ChatGPT → Google AI Overviews → Perplexity → Claude, and he estimated north of 90% of Gamma's prosumer AI acquisition came from ChatGPT. He suggested enterprise priorities flip toward Claude and Copilot/Bing, but labeled that as speculative — so validate against your own segment.

Q: How long before new content shows up as an AI citation?

A: It varies sharply by engine. Ravish observed Reddit content taking ~30–45 days to appear as a ChatGPT citation, while Perplexity picked up a new phrase as fast as the next day, and the host saw a Google AI Overview cite a query within a day. These are individual observations, not guaranteed timelines.

Q: How do I actually measure per-platform AEO?

A: Run separate prompt sets against each engine and report results per platform rather than as a single blended score. A per-engine tracker like Asva's brand visibility tracker shows mentions and citations for each surface independently, which is the only way to see the ~60% that diverges.

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Close

The mistake isn't running AEO — it's running one AEO. If Ravish Agrawal of Gamma is even roughly right that only ~30–40% of the playbook carries across engines, a single blended strategy optimizes for an average no real platform rewards. ChatGPT, Google AI Overviews, Perplexity, and Claude are four different games with four different scoreboards. Treat them that way: separate prompt sets, per-platform reporting, and assets matched to how each engine sources answers.

Start with visibility. Run your free AI Visibility Report, explore per-engine tracking inside the Asva app, or book 30 minutes to map a platform-by-platform plan for your brand.

All podcast figures are attributed to Ravish Agrawal of Gamma or the Distribution Podcast host and reflect one team's experience, not Asva measurement. Items marked ⚠️ are unverified recalled claims and should be confirmed before external citation.